China
Africa
Explore chapters and articles related to this topic
Role of Business Intelligence and HR Planning in Modern Industrialization
Published in Deepmala Singh, Anurag Singh, Amizan Omar, S.B. Goyal, Business Intelligence and Human Resource Management, 2023
This subsystem manages the collection, storage, and structuring of data into databases in the following formats: Data LakeData Lake is a huge set of raw data that are stored in the native format for a purpose not yet defined.Data WarehouseData Warehouse is a storehouse for organized, filtered, and processed data.Data MartData Mart is a subset of a data warehouse, but it holds data only for a specific department or line of business, such as sales, finance, or HRs.Operational Data StoreOperational Data Store is a snapshot of data gathered from multiple transactional systems for operational reporting.
Instigation and Development of Data Science
Published in Pallavi Vijay Chavan, Parikshit N Mahalle, Ramchandra Mangrulkar, Idongesit Williams, Data Science, 2022
Priyali Sakhare, Pallavi Vijay Chavan, Pournima Kulkarni, Ashwini Sarode
Firstly, we need to check whether there is access for the data to the company. Then, we have to check the quality of the data that is available in the company. Many companies have the habit of keeping the key data, so cleaning of data can be already done. Mainly, the data can be stored in data warehouses, data marts, databases, etc. Data warehouse is a system where it combines the data from different sources into a central repository to store the data and support data mining, machine learning, and business intelligence. Data mart is a subset of a data warehouse where it focuses on a specific area which only allows the authorized user to quickly access critical data without wasting time over finding through an entire data warehouse. A database is used to store the data.
Data Lakes: A Panacea for Big Data Problems, Cyber Safety Issues, and Enterprise Security
Published in Mohiuddin Ahmed, Nour Moustafa, Abu Barkat, Paul Haskell-Dowland, Next-Generation Enterprise Security and Governance, 2022
A. N. M. Bazlur Rashid, Mohiuddin Ahmed, Abu Barkat Ullah
According to the multidimensional model, data warehouses are designed that defines the analytical axes or dimensions and subjects or facts. Hence, there are two types of tables that form the basis of a data warehouse – dimension tables and fact tables. The fact tables answer the W questions – who, what, when, and where. On the other hand, the dimension tables are supplemented from the databases based on the fields. In a data warehouse, data are extracted (E), transformed (T), and loaded (L), i.e., it follows ETL operations. Enterprise data from various operational databases are collected into a single data warehouse storage to execute ad-hoc queries, which can help retrieve business intelligence conveniently. Transactional data remain in operational databases to provide online transaction processing (OLTP), such as daily business transactions. On the other hand, online analytical processing (OLAP) operations, such as data analysis, reviewing historical data, and analytical systems, perform data correlations. All the complex ad-hoc queries run in the data warehouse, which is built for analytical purposes. Data can be loaded in batch to the data warehouse. Data analytics can be performed on the stored data in the warehouse for better decision-making of the enterprise and for obtaining valuable insights [16].
An efficient approach for land record classification and information retrieval in data warehouse
Published in International Journal of Computers and Applications, 2021
C. B. David Joel Kishore, T. Bhaskara Reddy
A data warehouse is a structure used for the data analysis and storing a large amount of information. It is a central repository to store all kinds of data [1]. The data is one of the valuable resources for any government. It helps the planners and decision makers for taking decisions easily [2]. Government decision-makers have some difficulties in finding meaningful information to make special reports on time. They are dependent on IT staff to prepare the report [3]. However, a traditional database management system is unable to fulfill the requirements of mining data for useful information [4]. The operation of ETL (Extract, Transform and Load) is the most important part in data warehouse system development [5–7]. Big data is a term used for data sets that are so large or complex. It includes numerous challenges such as analysis, capture, data duration, search, sharing, storage, transfer, visualization, querying, and updating and information privacy [8]. Accuracy in big data may lead to more confident decision-making and better decisions [9].
Weighting the challenges to the effectiveness of business intelligence systems in organisations: an empirical study of government organisations in Saudi Arabia
Published in Journal of Decision Systems, 2020
In addition, the ability to enhance the value of BI depends on the capability to integrate the existing technical platforms and databases in order to collect and share data through a well-defined technological architecture and data standards (IşıK et al., 2013; Nam et al., 2019). Therefore, data integration capability is another antecedent to the effectiveness of BI systems in organisations. Data integration involves linking various internal and external data sources through different technological tools – such as a data warehouse – to provide a unified view of the business data. The main function of a data warehouse is to gather accurate, clean, and detailed data from different sources to enable the performance of in-depth analyses suited to support organisational decision-making (Yoon, 2008). Prior research has emphasised that integration is crucial for the successful implementation of a BI system (IşıK et al., 2013; A. Popovič et al., 2012).
Systems Dynamics-Based Modeling of Data Warehouse Quality
Published in Journal of Computer Information Systems, 2019
Girish H. Subramanian, Kai Wang
Generally speaking, as the core component of business intelligence, data warehouse is a system designed for data analysis and reporting. [8] Also, as defined by Kimball, [9] data warehouse can provide a data transaction process (e.g., extract-transform-load (ETL)) that is specifically structured for integrating data from multiple heterogeneous information sources and transforming them into a multidimensional representation for decision support applications. On the other hand, for example, with the ETL process continuously refreshing huge amounts of data from all kinds of sources, some problems (e.g., dirty data) exist. To avoid the garbage in, garbage out (GIGO) situation and effectively take advantage of data warehouse technology, the data warehouse quality and process management (DWQPM) should be standardized to better support the data warehouse. According to a survey that discussed the benefits of high data warehouse quality in 2001, [10] around 64% of respondents claimed that high quality can bring great confidence in analytical systems, single version of the truth and increased customer satisfaction; and the rest 36% insisted that there was less time spent reconciling data, reduced cost, and increased revenues.